How to Regulate a Gene: To Repress or to Activate?
The MIT Faculty has made this article openly available. Please share
how this access benefits you. Your story matters.
Citation
Slavov, Nikolai, and Alexander van Oudenaarden. “How to
Regulate a Gene: To Repress or to Activate?” Molecular Cell 46,
no. 5 (June 2012): 551–552. © 2012 Elsevier Inc.
As Published
http://dx.doi.org/10.1016/j.molcel.2012.05.035
Publisher
Elsevier
Version
Final published version
Accessed
Wed May 25 22:41:25 EDT 2016
Citable Link
http://hdl.handle.net/1721.1/96355
Terms of Use
Article is made available in accordance with the publisher's policy
and may be subject to US copyright law. Please refer to the
publisher's site for terms of use.
Detailed Terms
Molecular Cell
Previews
How to Regulate a Gene: To Repress or to Activate?
Nikolai Slavov1,2 and Alexander van Oudenaarden1,2,3,*
1Department
of Physics
of Biology
Massachusetts Institute of Technology, Cambridge MA 02139, USA
3Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, Uppsalalaan 8,
3584 CT Utrecht, The Netherlands
*Correspondence: avo1@mit.edu
DOI 10.1016/j.molcel.2012.05.035
2Department
Gene-expression responses to an input can depend on growth conditions; in this issue, Sasson et al. (2012)
show that this dependence is lower when the input results in a high degree of promoter occupancy.
All biological regulatory processes involve intermolecular interactions. Strong
binding between the interacting molecules, for example a transcription factor
(TF) and its cognate DNA promoter, can
result in high specificity of signaling, since
the TF is tightly bound to its cognate
promoter and, thus, excluding other nonspecific interactions, which can result
in condition-specific influences on the
signal transduction (Shinar et al., 2006).
Such tight binding, however, limits the
dynamical range of the regulatory effect
only to a regime of high-promoter occupancy by the cognate TF. Thus, signal
transduction is constrained by a tradeoff between the dynamical range of the
input signals (TF) and the conditionsdependent difference in the input-output
response function (which quantifies
the relation between input and downstream gene expression). Sasson et al.
(2012) combine well-controlled experiments and a simple model to demonstrate
an elegant solution to this trade-off in
E. coli.
Sasson et al. (2012) construct E. coli
strains in which a fluorescent reporter
(output) is transcribed under the control
of cAMP-receptor protein (CRP) that depending on the promoter acts either as
an activator or as a repressor (input).
Since the activity of CRP is modulated
by cAMP, the input in these strains can
be controlled easily by growing the strains
across different concentrations of cAMP,
and the resulting fluorescent signal can
be quantified accurately. This experimental design allows answering fundamental questions: Does the input-output
response function depend on the growth
conditions? Is this dependence affected
by the input level (cAMP concentration)
or by the mode of regulation, activation
versus repression? The authors found
that the input-output response function
was less dependent on the growth conditions, for both the activating and the repressing CRP, in the regime when the
CRP promoter occupancy was high.
This result is consistent with earlier theoretical predictions (Shinar et al., 2006)
and corroborates the idea that highpromoter occupancy by its cognate TF
may prevent nonspecific binding—and
thus result in more similar input-output
response functions across different
growth conditions.
Such high fidelity of the input-output
response function, however, is limited
only to input levels that result in highpromoter occupancies, raising another
intriguing question: Is it possible to overcome this limitation and make the inputoutput function robust to changes in the
growth conditions over a wider dynamical
range? One possibility is to place a gene
under the control of both a repressor,
which improves fidelity when the gene is
lowly expressed, and an activator, which
improves fidelity when the gene is highly
expressed. As an example of a promoter
regulated by two regulators, Sasson
et al. (2012) studied the regulation of a
classical system, the lac operon. By
modulating both an activator of the lac
promoter, CRP, and a repressor, LacI,
the authors were able to analyze the
differences in promoter activity across
equiexpression lines—that is, combinations of activator and repressor activities
resulting in equal promoter activity. This
is a particularly ingenious aspect of the
experimental design that allows sepa-
rating promoter occupancy (fraction of
bound binding sites) from other confounding variables and obtaining a clear
result: Controlling for other variables,
the higher the promoter occupancy, the
higher the similarity in the input-output
function across conditions.
The work of Sasson et al. (2012) not
only provides a concrete and compelling
example for a design principle that can
reduce undesired condition-dependent
influences on transcription, but also
has numerous broader implications that
open avenues for further research. One
such implication is that the principles suggested by Sasson et al. (2012) may not be
limited to the interactions between TFs
and their cognate promoters but likely
extend to other regulatory interactions,
such as protein-protein, protein-small
molecule, or RNA-microRNA interactions.
Indeed, the idea that the tight binding of
a ligand to its cognate regulatory site
can prevent no-specific interactions by
exclusion is quite general, and it seems
likely that such tight binding among regulatory proteins contributes to the fidelity
of signaling in other contexts. Another
important implication concerns the nonredundant function of multiple regulators
that operate in parallel. Each regulator
may be optimized to increase the signaling fidelity over a part of the dynamical
range, low or high level of signaling, and
thus contribute to nonredundant functions. The results of Sasson et al. (2012)
also raise the question of what the effect
and significance of promoter occupancy
is when the input signal is oscillating,
such as oscillating nuclear localization of
transcription factors or genome-wide
transcriptional oscillations (Cai et al.,
Molecular Cell 46, June 8, 2012 ª2012 Elsevier Inc. 551
Molecular Cell
Previews
2008; Slavov et al., 2011). The mode of
gene regulation affects the variability in
single-cell responses (Munsky et al.,
2012), raising another exciting question:
Can high-promoter occupancy also reduce variability among the input-output
responses of single cells? These implications and questions provide a fertile
ground for further work characteriz-
ing the design principles of signal
transduction.
Sasson, V., Shachrai, I., Bren, A., Dekel, E., and
Alon, U. (2012). Mol. Cell, (May): 25.
REFERENCES
Shinar, G., Dekel, E., Tlusty, T., and Alon, U. (2006).
Proc. Natl. Acad. Sci. USA 103, 3999–4004.
Cai, L., Dalal, C.K., and Elowitz, M.B. (2008).
Nature 455, 485–490.
Munsky, B., Neuert, G., and van Oudenaarden, A.
(2012). Science 336, 183–187.
Slavov, N., Macinskas, J., Caudy, A., and Botstein,
D. (2011). Proc. Natl. Acad. Sci. USA 108, 19090–
19095.
When Death Was Young:
An Ancestral Apoptotic Network in Bacteria
Didac Carmona-Gutierrez,1 Guido Kroemer,2,3,4,5,6 and Frank Madeo1,*
1Institute
of Molecular Biosciences, University of Graz, 8010 Graz, Austria
U848, F-94805 Villejuif, France
3Metabolomics Platform, Institut Gustave Roussy, F-94805 Villejuif, France
4Centre de Recherche des Cordeliers, 75006 Paris, France
5Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, 750015 Paris, France
6Université Paris Descartes, Sorbonne Paris Cité, 75679 Paris, France
*Correspondence: frank.madeo@uni-graz.at
DOI 10.1016/j.molcel.2012.05.032
2INSERM,
In this issue of Molecular Cell, Dwyer et al. (2012) characterize a RecA-dependent and ClpXP-regulated
pathway that controls the acquisition of several apoptotic markers upon bactericidal treatment of prokaryotes, placing the hypothetical origin of apoptosis further downstream in evolution.
In metazoans, the life span of individual
cells is regulated by an integrated suicide
system (programmed cell death, PCD)
that can be activated when cells become
superfluous, accumulate damage, or
menace organismal fitness. Among the
distinct subroutines constituting PCD,
apoptosis represents the best-studied
one. Apoptotic death is a structurally
and functionally conserved process in
thus far that it is also observed in unicellular eukaryotes, such as protozoan
parasites or yeast (Carmona-Gutierrez
et al., 2010; Madeo et al., 1997). Dwyer
et al. (2012) provide phenotypic and
mechanistic evidence that may expand
the evolutionary conservation frame of
apoptosis into the realm of prokaryotes.
The authors demonstrate that bacterial
cell death induced by treatment with different bactericidal antibiotics is accompanied by several biochemical markers
of apoptosis, including DNA fragmentation, chromosomal condensation, expo-
sure of phosphatidylserine to the outer
leaflet of the plasma membrane, and
dissipation of membrane potential (Dwyer
et al., 2012). These results add to previous
work by the same group (Dwyer et al.,
2007; Kohanski et al., 2007) showing
that bactericidal antibiotics promote the
generation of reactive oxygen species
(ROS), which are crucial apoptotic regulators in multicellular as well as in unicellular eukaryotes (Herker et al., 2004;
Simon et al., 2000). In bacteria, ROS
seem to play a similar role, since suppressing their formation reduces druginduced cell death (Dwyer et al., 2007)
as well as DNA fragmentation (Dwyer
et al., 2012).
Now, Dwyer et al. (2012) identify and
characterize RecA, a multifunctional protein crucial for DNA maintenance and
repair, as an additional player involved in
the antibiotic-triggered apoptotic demise
of bacteria. Consistent with this finding,
RecA plays a critical role in the recently
552 Molecular Cell 46, June 8, 2012 ª2012 Elsevier Inc.
described apoptosis-like death (ALD)
pathway of E. coli (Erental et al., 2012).
Dwyer et al. (2012) extend these observations by showing that the cell stresstriggered conversion of RecA into its
active form is a prerequisite for its contribution to cell-death induction (Dwyer
et al., 2012). The lethal activity of active
RecA is thereby negatively regulated
by the ClpP protease complex ClpXP.
These factors also dampen the LexAregulated bacterial DNA-damage (or
SOS) stress response, which is necessary
for the efficient induction of apoptosis
in response to cellular stress (Dwyer
et al., 2012).
In this network of interacting regulators,
RecA seems to function in a similar
fashion as do caspases, the central executionary cysteine proteases in many
scenarios of mammalian apoptosis.
Indeed, RecA can bind and hydrolyze
synthetic caspase substrates and appears to be the only bacterial enzyme to